循环遍历此数据的最快方法

时间：2018-08-08 19:37:26

标签： python python-3.x pandas numpy dataframe 

      Time          Entrysig     Exitsig
    7/16/2018 12:30 LongEntry   
    7/16/2018 13:00             LongExit
    7/16/2018 13:30     
    7/16/2018 14:00     
    7/16/2018 14:30     
    7/16/2018 15:00     
    7/16/2018 15:30 ShortEntry  LongExit <---wrong
    7/16/2018 17:00     
    7/16/2018 17:30     
    7/16/2018 18:00     
    7/16/2018 18:30     
    7/16/2018 19:00 LongEntry   ShortExit
    7/16/2018 19:30     
    7/16/2018 20:00     
    7/16/2018 20:30     
    7/16/2018 21:00 ShortEntry  LongExit
    7/16/2018 21:30     
    7/16/2018 22:00     
    7/16/2018 22:30     
    7/16/2018 23:00     
    7/16/2018 23:30     
    7/17/2018 0:00      
    7/17/2018 0:30      
    7/17/2018 1:00      
    7/17/2018 1:30      
    7/17/2018 2:00  LongEntry   ShortExit
    7/17/2018 2:30      
    7/17/2018 3:00

我有一个类似上面的数据框。我试图在不做循环（如果可能的话）的情况下使其他2列多头和空头。在进入多头之后应该说“多头”，并持续多长时间直到发出“长头”信号。与“短”列相同。如果不是长，短或留空，则可以为0。两种方法都可以。

注意： 我的退出信号有一个问题，即使我已经退出交易，它仍然会返回退出信号，因此它应该忽略这些信号。 （不能两次退出同一多头交易）。我已经指出了一个错误的地方。我也在努力解决此问题，但这是一个单独的问题。

输出：

Date             EntrySig   ExitSig     Long    Short
7/16/2018 12:30 LongEntry               Long    
7/16/2018 13:00            LongExit     Long    
7/16/2018 13:30             
7/16/2018 14:00             
7/16/2018 14:30             
7/16/2018 15:00             
7/16/2018 15:30 ShortEntry  LongExit            Short
7/16/2018 17:00                                 Short
7/16/2018 17:30                                 Short
7/16/2018 18:00                                 Short
7/16/2018 18:30                                 Short
7/16/2018 19:00 LongEntry   ShortExit   Long    Short
7/16/2018 19:30                         Long    
7/16/2018 20:00                         Long    
7/16/2018 20:30                         Long    
7/16/2018 21:00 ShortEntry  LongExit    Long    Short
7/16/2018 21:30                                 Short
7/16/2018 22:00                                 Short
7/16/2018 22:30                                 Short
7/16/2018 23:00                                 Short
7/16/2018 23:30                                 Short
7/17/2018 0:00                                  Short
7/17/2018 0:30                                  Short
7/17/2018 1:00                                  Short
7/17/2018 1:30                                  Short
7/17/2018 2:00  LongEntry   ShortExit   Long    Short
7/17/2018 2:30                          Long    
7/17/2018 3:00                          Long

我尝试使用迭代：

ls = []
trades = bt_data.reset_index()
for i in trades.index:
    if trades.iloc[i, trades.columns.get_loc('EntrySignal')] == 'LongEntry':
        ls.append('Long')
    elif i==0:
        ls.append('Flat')
    elif np.logical_and(ls[i-1] == 'Long', 
         trades.iloc[i-1, trades.columns.get_loc('ExitSignal')] 
         !='LongExit'):
            ls.append('Long')
        else:
            ls.append('Flat')

将数据作为dict：

{'Date_Time': {0: Timestamp('2018-07-16 03:30:00'), 1: Timestamp('2018-07-16 04:00:00'), 2: Timestamp('2018-07-16 04:30:00'), 3: Timestamp('2018-07-16 05:00:00'), 4: Timestamp('2018-07-16 05:30:00'), 5: Timestamp('2018-07-16 06:00:00'), 6: Timestamp('2018-07-16 06:30:00'), 7: Timestamp('2018-07-16 07:00:00'), 8: Timestamp('2018-07-16 07:30:00'), 9: Timestamp('2018-07-16 08:00:00'), 10: Timestamp('2018-07-16 08:30:00'), 11: Timestamp('2018-07-16 09:00:00'), 12: Timestamp('2018-07-16 09:30:00'), 13: Timestamp('2018-07-16 10:00:00'), 14: Timestamp('2018-07-16 10:30:00'), 15: Timestamp('2018-07-16 11:00:00'), 16: Timestamp('2018-07-16 11:30:00'), 17: Timestamp('2018-07-16 12:00:00'), 18: Timestamp('2018-07-16 12:30:00'), 19: Timestamp('2018-07-16 13:00:00'), 20: Timestamp('2018-07-16 13:30:00'), 21: Timestamp('2018-07-16 14:00:00'), 22: Timestamp('2018-07-16 14:30:00'), 23: Timestamp('2018-07-16 15:00:00'), 24: Timestamp('2018-07-16 15:30:00'), 25: Timestamp('2018-07-16 17:00:00'), 26: Timestamp('2018-07-16 17:30:00'), 27: Timestamp('2018-07-16 18:00:00'), 28: Timestamp('2018-07-16 18:30:00'), 29: Timestamp('2018-07-16 19:00:00'), 30: Timestamp('2018-07-16 19:30:00'), 31: Timestamp('2018-07-16 20:00:00'), 32: Timestamp('2018-07-16 20:30:00'), 33: Timestamp('2018-07-16 21:00:00'), 34: Timestamp('2018-07-16 21:30:00'), 35: Timestamp('2018-07-16 22:00:00'), 36: Timestamp('2018-07-16 22:30:00'), 37: Timestamp('2018-07-16 23:00:00'), 38: Timestamp('2018-07-16 23:30:00'), 39: Timestamp('2018-07-17 00:00:00'), 40: Timestamp('2018-07-17 00:30:00'), 41: Timestamp('2018-07-17 01:00:00'), 42: Timestamp('2018-07-17 01:30:00'), 43: Timestamp('2018-07-17 02:00:00'), 44: Timestamp('2018-07-17 02:30:00'), 45: Timestamp('2018-07-17 03:00:00'), 46: Timestamp('2018-07-17 03:30:00'), 47: Timestamp('2018-07-17 04:00:00'), 48: Timestamp('2018-07-17 04:30:00'), 49: Timestamp('2018-07-17 05:00:00'), 50: Timestamp('2018-07-17 05:30:00'), 51: Timestamp('2018-07-17 06:00:00'), 52: Timestamp('2018-07-17 06:30:00'), 53: Timestamp('2018-07-17 07:00:00'), 54: Timestamp('2018-07-17 07:30:00'), 55: Timestamp('2018-07-17 08:00:00'), 56: Timestamp('2018-07-17 08:30:00'), 57: Timestamp('2018-07-17 09:00:00'), 58: Timestamp('2018-07-17 09:30:00'), 59: Timestamp('2018-07-17 10:00:00'), 60: Timestamp('2018-07-17 10:30:00'), 61: Timestamp('2018-07-17 11:00:00'), 62: Timestamp('2018-07-17 11:30:00'), 63: Timestamp('2018-07-17 12:00:00'), 64: Timestamp('2018-07-17 12:30:00'), 65: Timestamp('2018-07-17 13:00:00'), 66: Timestamp('2018-07-17 13:30:00'), 67: Timestamp('2018-07-17 14:00:00'), 68: Timestamp('2018-07-17 14:30:00'), 69: Timestamp('2018-07-17 15:00:00'), 70: Timestamp('2018-07-17 15:30:00'), 71: Timestamp('2018-07-17 17:00:00'), 72: Timestamp('2018-07-17 17:30:00'), 73: Timestamp('2018-07-17 18:00:00'), 74: Timestamp('2018-07-17 18:30:00'), 75: Timestamp('2018-07-17 19:00:00')}, 'EntrySignal': {0: '', 1: '', 2: '', 3: '', 4: '', 5: '', 6: '', 7: '', 8: '', 9: '', 10: '', 11: '', 12: '', 13: '', 14: '', 15: '', 16: '', 17: '', 18: '', 19: 'LongEntry', 20: '', 21: '', 22: '', 23: '', 24: '', 25: 'ShortEntry', 26: '', 27: '', 28: '', 29: '', 30: 'LongEntry', 31: '', 32: '', 33: '', 34: 'ShortEntry', 35: '', 36: '', 37: '', 38: '', 39: '', 40: '', 41: '', 42: '', 43: '', 44: 'LongEntry', 45: '', 46: '', 47: '', 48: 'ShortEntry', 49: '', 50: 'LongEntry', 51: '', 52: 'ShortEntry', 53: '', 54: '', 55: '', 56: '', 57: '', 58: '', 59: '', 60: '', 61: 'LongEntry', 62: '', 63: '', 64: '', 65: '', 66: '', 67: '', 68: 'ShortEntry', 69: '', 70: '', 71: '', 72: '', 73: '', 74: 'LongEntry', 75: ''}, 'ExitSignal': {0: '', 1: '', 2: '', 3: '', 4: '', 5: '', 6: '', 7: '', 8: '', 9: '', 10: '', 11: '', 12: '', 13: '', 14: '', 15: '', 16: '', 17: '', 18: '', 19: 'ShortExit', 20: 'LongExit', 21: '', 22: '', 23: '', 24: '', 25: 'LongExit', 26: '', 27: '', 28: '', 29: '', 30: 'ShortExit', 31: '', 32: '', 33: '', 34: 'LongExit', 35: '', 36: '', 37: '', 38: '', 39: '', 40: '', 41: '', 42: '', 43: '', 44: 'ShortExit', 45: '', 46: '', 47: '', 48: 'LongExit', 49: '', 50: 'ShortExit', 51: '', 52: 'LongExit', 53: '', 54: '', 55: 'ShortExit', 56: '', 57: '', 58: '', 59: '', 60: '', 61: 'ShortExit', 62: 'LongExit', 63: '', 64: '', 65: '', 66: '', 67: '', 68: 'LongExit', 69: '', 70: 'ShortExit', 71: '', 72: '', 73: '', 74: 'ShortExit', 75: ''}}

1 个答案:

答案 0 :(得分：0)

如果您可以删除错误的出口，则可以通过累加条目并累计去除出口并将值（1,0）映射到（长/短，不长/不短）来实现：

df['Long'] = (df['EntrySignal'] == 'LongEntry').cumsum()  - (df['ExitSignal'] == 'LongExit').cumsum()
df['Short'] = (df['EntrySignal'] == 'ShortEntry').cumsum() - (df['ExitSignal'] == 'ShortExit').cumsum()

df['Long'] = df['Long'].map({1: 'Long', 0: ''})
df['Short'] = df['Short'].map({1: 'Short', 0: ''})

注意：错误输入的值将小于0，并且您将需要更复杂的逻辑。

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